Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations97373
Missing cells495037
Missing cells (%)15.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.3 MiB
Average record size in memory703.4 B

Variable types

DateTime1
Categorical5
Numeric21
Text4
Unsupported1

Alerts

CANCELLED has constant value "0.0"Constant
AIRLINE is highly overall correlated with AIRLINE_CODE and 2 other fieldsHigh correlation
AIRLINE_CODE is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
AIRLINE_DOT is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
AIR_TIME is highly overall correlated with CRS_ELAPSED_TIME and 3 other fieldsHigh correlation
ARR_DELAY is highly overall correlated with DEP_DELAY and 1 other fieldsHigh correlation
ARR_TIME is highly overall correlated with CRS_ARR_TIME and 4 other fieldsHigh correlation
CRS_ARR_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
CRS_DEP_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
CRS_ELAPSED_TIME is highly overall correlated with AIR_TIME and 2 other fieldsHigh correlation
DELAY_DUE_CARRIER is highly overall correlated with DIVERTEDHigh correlation
DELAY_DUE_LATE_AIRCRAFT is highly overall correlated with DIVERTEDHigh correlation
DELAY_DUE_NAS is highly overall correlated with DIVERTEDHigh correlation
DELAY_DUE_SECURITY is highly overall correlated with DIVERTEDHigh correlation
DELAY_DUE_WEATHER is highly overall correlated with DIVERTEDHigh correlation
DEP_DELAY is highly overall correlated with ARR_DELAYHigh correlation
DEP_TIME is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
DISTANCE is highly overall correlated with AIR_TIME and 2 other fieldsHigh correlation
DIVERTED is highly overall correlated with AIR_TIME and 7 other fieldsHigh correlation
DOT_CODE is highly overall correlated with AIRLINE and 2 other fieldsHigh correlation
ELAPSED_TIME is highly overall correlated with AIR_TIME and 3 other fieldsHigh correlation
WHEELS_OFF is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
WHEELS_ON is highly overall correlated with ARR_TIME and 4 other fieldsHigh correlation
DIVERTED is highly imbalanced (97.6%)Imbalance
CANCELLATION_CODE has 97373 (100.0%) missing valuesMissing
DELAY_DUE_CARRIER has 79381 (81.5%) missing valuesMissing
DELAY_DUE_WEATHER has 79381 (81.5%) missing valuesMissing
DELAY_DUE_NAS has 79381 (81.5%) missing valuesMissing
DELAY_DUE_SECURITY has 79381 (81.5%) missing valuesMissing
DELAY_DUE_LATE_AIRCRAFT has 79381 (81.5%) missing valuesMissing
DELAY_DUE_SECURITY is highly skewed (γ1 = 50.5875775)Skewed
CANCELLATION_CODE is an unsupported type, check if it needs cleaning or further analysisUnsupported
DEP_DELAY has 4850 (5.0%) zerosZeros
ARR_DELAY has 1853 (1.9%) zerosZeros
DELAY_DUE_CARRIER has 8088 (8.3%) zerosZeros
DELAY_DUE_WEATHER has 16957 (17.4%) zerosZeros
DELAY_DUE_NAS has 9248 (9.5%) zerosZeros
DELAY_DUE_SECURITY has 17890 (18.4%) zerosZeros
DELAY_DUE_LATE_AIRCRAFT has 9369 (9.6%) zerosZeros

Reproduction

Analysis started2025-07-08 18:59:38.583893
Analysis finished2025-07-08 19:00:10.232913
Duration31.65 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct1704
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2019-01-01 00:00:00
Maximum2023-08-31 00:00:00
2025-07-08T21:00:10.286713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:10.368711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AIRLINE
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.1 MiB
Southwest Airlines Co.
18496 
Delta Air Lines Inc.
12875 
American Airlines Inc.
12508 
SkyWest Airlines Inc.
11075 
United Air Lines Inc.
8314 
Other values (13)
34105 

Length

Max length34
Median length22
Mean length19.59916
Min length9

Characters and Unicode

Total characters1908429
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAllegiant Air
2nd rowPSA Airlines Inc.
3rd rowSouthwest Airlines Co.
4th rowSouthwest Airlines Co.
5th rowDelta Air Lines Inc.

Common Values

ValueCountFrequency (%)
Southwest Airlines Co. 18496
19.0%
Delta Air Lines Inc. 12875
13.2%
American Airlines Inc. 12508
12.8%
SkyWest Airlines Inc. 11075
11.4%
United Air Lines Inc. 8314
8.5%
Republic Airline 4796
 
4.9%
Envoy Air 3847
 
4.0%
Endeavor Air Inc. 3669
 
3.8%
JetBlue Airways 3609
 
3.7%
PSA Airlines Inc. 3557
 
3.7%
Other values (8) 14627
15.0%

Length

2025-07-08T21:00:10.450289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 60552
20.2%
airlines 54810
18.3%
air 34158
11.4%
lines 24322
8.1%
southwest 18496
 
6.2%
co 18496
 
6.2%
delta 12875
 
4.3%
american 12508
 
4.2%
skywest 11075
 
3.7%
united 8314
 
2.8%
Other values (18) 44370
14.8%

Most occurring characters

ValueCountFrequency (%)
i 219621
11.5%
202603
 
10.6%
n 178270
 
9.3%
e 169965
 
8.9%
r 122254
 
6.4%
s 118974
 
6.2%
A 118492
 
6.2%
l 87605
 
4.6%
t 80431
 
4.2%
. 79048
 
4.1%
Other values (33) 531166
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1908429
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 219621
11.5%
202603
 
10.6%
n 178270
 
9.3%
e 169965
 
8.9%
r 122254
 
6.4%
s 118974
 
6.2%
A 118492
 
6.2%
l 87605
 
4.6%
t 80431
 
4.2%
. 79048
 
4.1%
Other values (33) 531166
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1908429
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 219621
11.5%
202603
 
10.6%
n 178270
 
9.3%
e 169965
 
8.9%
r 122254
 
6.4%
s 118974
 
6.2%
A 118492
 
6.2%
l 87605
 
4.6%
t 80431
 
4.2%
. 79048
 
4.1%
Other values (33) 531166
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1908429
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 219621
11.5%
202603
 
10.6%
n 178270
 
9.3%
e 169965
 
8.9%
r 122254
 
6.4%
s 118974
 
6.2%
A 118492
 
6.2%
l 87605
 
4.6%
t 80431
 
4.2%
. 79048
 
4.1%
Other values (33) 531166
27.8%

AIRLINE_DOT
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
Southwest Airlines Co.: WN
18496 
Delta Air Lines Inc.: DL
12875 
American Airlines Inc.: AA
12508 
SkyWest Airlines Inc.: OO
11075 
United Air Lines Inc.: UA
8314 
Other values (13)
34105 

Length

Max length38
Median length26
Mean length23.59916
Min length13

Characters and Unicode

Total characters2297921
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAllegiant Air: G4
2nd rowPSA Airlines Inc.: OH
3rd rowSouthwest Airlines Co.: WN
4th rowSouthwest Airlines Co.: WN
5th rowDelta Air Lines Inc.: DL

Common Values

ValueCountFrequency (%)
Southwest Airlines Co.: WN 18496
19.0%
Delta Air Lines Inc.: DL 12875
13.2%
American Airlines Inc.: AA 12508
12.8%
SkyWest Airlines Inc.: OO 11075
11.4%
United Air Lines Inc.: UA 8314
8.5%
Republic Airline: YX 4796
 
4.9%
Envoy Air: MQ 3847
 
4.0%
Endeavor Air Inc.: 9E 3669
 
3.8%
JetBlue Airways: B6 3609
 
3.7%
PSA Airlines Inc.: OH 3557
 
3.7%
Other values (8) 14627
15.0%

Length

2025-07-08T21:00:10.519289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 60552
15.2%
airlines 54810
13.8%
air 34158
 
8.6%
lines 24322
 
6.1%
southwest 18496
 
4.7%
co 18496
 
4.7%
wn 18496
 
4.7%
delta 12875
 
3.2%
dl 12875
 
3.2%
american 12508
 
3.1%
Other values (36) 129761
32.7%

Most occurring characters

ValueCountFrequency (%)
299976
 
13.1%
i 219621
 
9.6%
n 178270
 
7.8%
e 169965
 
7.4%
A 156195
 
6.8%
r 122254
 
5.3%
s 118974
 
5.2%
: 97373
 
4.2%
l 87605
 
3.8%
t 80431
 
3.5%
Other values (45) 767257
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2297921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
299976
 
13.1%
i 219621
 
9.6%
n 178270
 
7.8%
e 169965
 
7.4%
A 156195
 
6.8%
r 122254
 
5.3%
s 118974
 
5.2%
: 97373
 
4.2%
l 87605
 
3.8%
t 80431
 
3.5%
Other values (45) 767257
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2297921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
299976
 
13.1%
i 219621
 
9.6%
n 178270
 
7.8%
e 169965
 
7.4%
A 156195
 
6.8%
r 122254
 
5.3%
s 118974
 
5.2%
: 97373
 
4.2%
l 87605
 
3.8%
t 80431
 
3.5%
Other values (45) 767257
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2297921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
299976
 
13.1%
i 219621
 
9.6%
n 178270
 
7.8%
e 169965
 
7.4%
A 156195
 
6.8%
r 122254
 
5.3%
s 118974
 
5.2%
: 97373
 
4.2%
l 87605
 
3.8%
t 80431
 
3.5%
Other values (45) 767257
33.4%

AIRLINE_CODE
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
WN
18496 
DL
12875 
AA
12508 
OO
11075 
UA
8314 
Other values (13)
34105 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters194746
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG4
2nd rowOH
3rd rowWN
4th rowWN
5th rowDL

Common Values

ValueCountFrequency (%)
WN 18496
19.0%
DL 12875
13.2%
AA 12508
12.8%
OO 11075
11.4%
UA 8314
8.5%
YX 4796
 
4.9%
MQ 3847
 
4.0%
9E 3669
 
3.8%
B6 3609
 
3.7%
OH 3557
 
3.7%
Other values (8) 14627
15.0%

Length

2025-07-08T21:00:10.585290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 18496
19.0%
dl 12875
13.2%
aa 12508
12.8%
oo 11075
11.4%
ua 8314
8.5%
yx 4796
 
4.9%
mq 3847
 
4.0%
9e 3669
 
3.8%
b6 3609
 
3.7%
oh 3557
 
3.7%
Other values (8) 14627
15.0%

Most occurring characters

ValueCountFrequency (%)
A 37703
19.4%
O 25707
13.2%
N 21629
11.1%
W 18496
9.5%
L 12875
 
6.6%
D 12875
 
6.6%
U 8314
 
4.3%
Y 6829
 
3.5%
9 5817
 
3.0%
X 5451
 
2.8%
Other values (12) 39050
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 194746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 37703
19.4%
O 25707
13.2%
N 21629
11.1%
W 18496
9.5%
L 12875
 
6.6%
D 12875
 
6.6%
U 8314
 
4.3%
Y 6829
 
3.5%
9 5817
 
3.0%
X 5451
 
2.8%
Other values (12) 39050
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 194746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 37703
19.4%
O 25707
13.2%
N 21629
11.1%
W 18496
9.5%
L 12875
 
6.6%
D 12875
 
6.6%
U 8314
 
4.3%
Y 6829
 
3.5%
9 5817
 
3.0%
X 5451
 
2.8%
Other values (12) 39050
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 194746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 37703
19.4%
O 25707
13.2%
N 21629
11.1%
W 18496
9.5%
L 12875
 
6.6%
D 12875
 
6.6%
U 8314
 
4.3%
Y 6829
 
3.5%
9 5817
 
3.0%
X 5451
 
2.8%
Other values (12) 39050
20.1%

DOT_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19977.667
Minimum19393
Maximum20452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:10.647289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum19393
5-th percentile19393
Q119790
median19930
Q320368
95-th percentile20436
Maximum20452
Range1059
Interquartile range (IQR)578

Descriptive statistics

Standard deviation376.45574
Coefficient of variation (CV)0.018843829
Kurtosis-1.3026042
Mean19977.667
Median Absolute Deviation (MAD)374
Skewness-0.23202036
Sum1.9452853 × 109
Variance141718.93
MonotonicityNot monotonic
2025-07-08T21:00:10.716290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
19393 18496
19.0%
19790 12875
13.2%
19805 12508
12.8%
20304 11075
11.4%
19977 8314
8.5%
20452 4796
 
4.9%
20398 3847
 
4.0%
20363 3669
 
3.8%
20409 3609
 
3.7%
20397 3557
 
3.7%
Other values (8) 14627
15.0%
ValueCountFrequency (%)
19393 18496
19.0%
19687 655
 
0.7%
19690 984
 
1.0%
19790 12875
13.2%
19805 12508
12.8%
19930 3389
 
3.5%
19977 8314
8.5%
20304 11075
11.4%
20363 3669
 
3.8%
20366 620
 
0.6%
ValueCountFrequency (%)
20452 4796
4.9%
20436 2148
2.2%
20416 3133
3.2%
20409 3609
3.7%
20398 3847
4.0%
20397 3557
3.7%
20378 2033
2.1%
20368 1665
 
1.7%
20366 620
 
0.6%
20363 3669
3.8%

FL_NUMBER
Real number (ℝ)

Distinct6537
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2510.9279
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:10.795289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile263
Q11055
median2149
Q33791
95-th percentile5648
Maximum8819
Range8818
Interquartile range (IQR)2736

Descriptive statistics

Standard deviation1744.4586
Coefficient of variation (CV)0.69474659
Kurtosis-0.89400184
Mean2510.9279
Median Absolute Deviation (MAD)1332
Skewness0.50618462
Sum2.4449659 × 108
Variance3043135.9
MonotonicityNot monotonic
2025-07-08T21:00:10.869288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320 53
 
0.1%
676 52
 
0.1%
334 51
 
0.1%
710 47
 
< 0.1%
350 46
 
< 0.1%
55 46
 
< 0.1%
546 45
 
< 0.1%
61 45
 
< 0.1%
678 44
 
< 0.1%
431 44
 
< 0.1%
Other values (6527) 96900
99.5%
ValueCountFrequency (%)
1 27
< 0.1%
2 24
< 0.1%
3 30
< 0.1%
4 21
< 0.1%
5 27
< 0.1%
6 31
< 0.1%
7 21
< 0.1%
8 25
< 0.1%
9 14
< 0.1%
10 27
< 0.1%
ValueCountFrequency (%)
8819 1
< 0.1%
8795 1
< 0.1%
8783 1
< 0.1%
8771 1
< 0.1%
7439 1
< 0.1%
7438 1
< 0.1%
7436 1
< 0.1%
7434 2
< 0.1%
7429 2
< 0.1%
7426 1
< 0.1%

ORIGIN
Text

Distinct372
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
2025-07-08T21:00:11.041880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters292119
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPGD
2nd rowEWN
3rd rowABQ
4th rowPIT
5th rowLAX
ValueCountFrequency (%)
atl 5006
 
5.1%
dfw 4310
 
4.4%
ord 3937
 
4.0%
den 3775
 
3.9%
clt 3029
 
3.1%
lax 2814
 
2.9%
phx 2415
 
2.5%
sea 2409
 
2.5%
las 2341
 
2.4%
iah 2138
 
2.2%
Other values (362) 65199
67.0%
2025-07-08T21:00:11.268880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 32982
 
11.3%
L 27058
 
9.3%
S 24502
 
8.4%
D 23178
 
7.9%
T 16306
 
5.6%
C 14955
 
5.1%
O 14786
 
5.1%
M 12934
 
4.4%
F 12241
 
4.2%
W 11778
 
4.0%
Other values (16) 101399
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 32982
 
11.3%
L 27058
 
9.3%
S 24502
 
8.4%
D 23178
 
7.9%
T 16306
 
5.6%
C 14955
 
5.1%
O 14786
 
5.1%
M 12934
 
4.4%
F 12241
 
4.2%
W 11778
 
4.0%
Other values (16) 101399
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 32982
 
11.3%
L 27058
 
9.3%
S 24502
 
8.4%
D 23178
 
7.9%
T 16306
 
5.6%
C 14955
 
5.1%
O 14786
 
5.1%
M 12934
 
4.4%
F 12241
 
4.2%
W 11778
 
4.0%
Other values (16) 101399
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 32982
 
11.3%
L 27058
 
9.3%
S 24502
 
8.4%
D 23178
 
7.9%
T 16306
 
5.6%
C 14955
 
5.1%
O 14786
 
5.1%
M 12934
 
4.4%
F 12241
 
4.2%
W 11778
 
4.0%
Other values (16) 101399
34.7%
Distinct366
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
2025-07-08T21:00:11.403880image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.129677
Min length8

Characters and Unicode

Total characters1278476
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPunta Gorda, FL
2nd rowNew Bern/Morehead/Beaufort, NC
3rd rowAlbuquerque, NM
4th rowPittsburgh, PA
5th rowLos Angeles, CA
ValueCountFrequency (%)
tx 10694
 
4.7%
ca 10309
 
4.5%
fl 8328
 
3.7%
ga 5364
 
2.4%
il 5295
 
2.3%
chicago 5056
 
2.2%
atlanta 5006
 
2.2%
san 4921
 
2.2%
ny 4829
 
2.1%
new 4469
 
2.0%
Other values (442) 162431
71.6%
2025-07-08T21:00:11.610156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
129329
 
10.1%
a 97503
 
7.6%
, 97373
 
7.6%
o 71457
 
5.6%
e 67321
 
5.3%
t 63640
 
5.0%
n 62447
 
4.9%
l 57173
 
4.5%
i 49094
 
3.8%
r 46455
 
3.6%
Other values (48) 536684
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1278476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
129329
 
10.1%
a 97503
 
7.6%
, 97373
 
7.6%
o 71457
 
5.6%
e 67321
 
5.3%
t 63640
 
5.0%
n 62447
 
4.9%
l 57173
 
4.5%
i 49094
 
3.8%
r 46455
 
3.6%
Other values (48) 536684
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1278476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
129329
 
10.1%
a 97503
 
7.6%
, 97373
 
7.6%
o 71457
 
5.6%
e 67321
 
5.3%
t 63640
 
5.0%
n 62447
 
4.9%
l 57173
 
4.5%
i 49094
 
3.8%
r 46455
 
3.6%
Other values (48) 536684
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1278476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
129329
 
10.1%
a 97503
 
7.6%
, 97373
 
7.6%
o 71457
 
5.6%
e 67321
 
5.3%
t 63640
 
5.0%
n 62447
 
4.9%
l 57173
 
4.5%
i 49094
 
3.8%
r 46455
 
3.6%
Other values (48) 536684
42.0%

DEST
Text

Distinct376
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
2025-07-08T21:00:11.775649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters292119
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowSPI
2nd rowCLT
3rd rowDEN
4th rowSTL
5th rowSEA
ValueCountFrequency (%)
atl 5025
 
5.2%
dfw 4117
 
4.2%
ord 4046
 
4.2%
den 3832
 
3.9%
clt 3187
 
3.3%
lax 2742
 
2.8%
phx 2566
 
2.6%
las 2349
 
2.4%
sea 2279
 
2.3%
mco 2112
 
2.2%
Other values (366) 65118
66.9%
2025-07-08T21:00:12.000461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 32675
 
11.2%
L 27210
 
9.3%
S 24426
 
8.4%
D 23147
 
7.9%
T 16248
 
5.6%
C 15051
 
5.2%
O 15024
 
5.1%
M 13135
 
4.5%
F 12061
 
4.1%
P 11552
 
4.0%
Other values (16) 101590
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 32675
 
11.2%
L 27210
 
9.3%
S 24426
 
8.4%
D 23147
 
7.9%
T 16248
 
5.6%
C 15051
 
5.2%
O 15024
 
5.1%
M 13135
 
4.5%
F 12061
 
4.1%
P 11552
 
4.0%
Other values (16) 101590
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 32675
 
11.2%
L 27210
 
9.3%
S 24426
 
8.4%
D 23147
 
7.9%
T 16248
 
5.6%
C 15051
 
5.2%
O 15024
 
5.1%
M 13135
 
4.5%
F 12061
 
4.1%
P 11552
 
4.0%
Other values (16) 101590
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 32675
 
11.2%
L 27210
 
9.3%
S 24426
 
8.4%
D 23147
 
7.9%
T 16248
 
5.6%
C 15051
 
5.2%
O 15024
 
5.1%
M 13135
 
4.5%
F 12061
 
4.1%
P 11552
 
4.0%
Other values (16) 101590
34.8%
Distinct369
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
2025-07-08T21:00:12.144606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.096413
Min length8

Characters and Unicode

Total characters1275237
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowSpringfield, IL
2nd rowCharlotte, NC
3rd rowDenver, CO
4th rowSt. Louis, MO
5th rowSeattle, WA
ValueCountFrequency (%)
tx 10395
 
4.6%
ca 10270
 
4.5%
fl 8527
 
3.8%
il 5411
 
2.4%
ga 5388
 
2.4%
chicago 5191
 
2.3%
atlanta 5025
 
2.2%
san 4911
 
2.2%
ny 4648
 
2.1%
nc 4510
 
2.0%
Other values (445) 162047
71.6%
2025-07-08T21:00:12.359607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
128950
 
10.1%
a 97752
 
7.7%
, 97373
 
7.6%
o 71066
 
5.6%
e 67002
 
5.3%
t 63022
 
4.9%
n 62379
 
4.9%
l 56725
 
4.4%
i 48896
 
3.8%
r 45912
 
3.6%
Other values (48) 536160
42.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1275237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
128950
 
10.1%
a 97752
 
7.7%
, 97373
 
7.6%
o 71066
 
5.6%
e 67002
 
5.3%
t 63022
 
4.9%
n 62379
 
4.9%
l 56725
 
4.4%
i 48896
 
3.8%
r 45912
 
3.6%
Other values (48) 536160
42.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1275237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
128950
 
10.1%
a 97752
 
7.7%
, 97373
 
7.6%
o 71066
 
5.6%
e 67002
 
5.3%
t 63022
 
4.9%
n 62379
 
4.9%
l 56725
 
4.4%
i 48896
 
3.8%
r 45912
 
3.6%
Other values (48) 536160
42.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1275237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
128950
 
10.1%
a 97752
 
7.7%
, 97373
 
7.6%
o 71066
 
5.6%
e 67002
 
5.3%
t 63022
 
4.9%
n 62379
 
4.9%
l 56725
 
4.4%
i 48896
 
3.8%
r 45912
 
3.6%
Other values (48) 536160
42.0%

CRS_DEP_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct1235
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1328.0314
Minimum4
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:12.430606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile605
Q1915
median1320
Q31730
95-th percentile2120
Maximum2359
Range2355
Interquartile range (IQR)815

Descriptive statistics

Standard deviation485.08206
Coefficient of variation (CV)0.365264
Kurtosis-1.0313097
Mean1328.0314
Median Absolute Deviation (MAD)407
Skewness0.088856519
Sum1.293144 × 108
Variance235304.6
MonotonicityNot monotonic
2025-07-08T21:00:12.501254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 2004
 
2.1%
700 1647
 
1.7%
800 998
 
1.0%
830 691
 
0.7%
900 664
 
0.7%
630 641
 
0.7%
730 631
 
0.6%
1000 627
 
0.6%
1100 559
 
0.6%
615 516
 
0.5%
Other values (1225) 88395
90.8%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
14 3
 
< 0.1%
15 9
< 0.1%
16 1
 
< 0.1%
18 2
 
< 0.1%
ValueCountFrequency (%)
2359 112
0.1%
2358 10
 
< 0.1%
2357 3
 
< 0.1%
2356 8
 
< 0.1%
2355 52
0.1%
2354 1
 
< 0.1%
2353 2
 
< 0.1%
2352 3
 
< 0.1%
2351 3
 
< 0.1%
2350 28
 
< 0.1%

DEP_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct1332
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1330.8337
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:12.572254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile603
Q1918
median1325
Q31738
95-th percentile2133
Maximum2400
Range2399
Interquartile range (IQR)820

Descriptive statistics

Standard deviation498.81487
Coefficient of variation (CV)0.37481381
Kurtosis-0.9639315
Mean1330.8337
Median Absolute Deviation (MAD)411
Skewness0.040479117
Sum1.2958727 × 108
Variance248816.27
MonotonicityNot monotonic
2025-07-08T21:00:12.645255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 279
 
0.3%
556 253
 
0.3%
655 227
 
0.2%
557 222
 
0.2%
558 220
 
0.2%
559 214
 
0.2%
554 214
 
0.2%
600 202
 
0.2%
656 198
 
0.2%
700 188
 
0.2%
Other values (1322) 95156
97.7%
ValueCountFrequency (%)
1 13
< 0.1%
2 8
< 0.1%
3 9
< 0.1%
4 14
< 0.1%
5 8
< 0.1%
6 11
< 0.1%
7 9
< 0.1%
8 11
< 0.1%
9 6
< 0.1%
10 10
< 0.1%
ValueCountFrequency (%)
2400 8
 
< 0.1%
2359 9
< 0.1%
2358 16
< 0.1%
2357 20
< 0.1%
2356 14
< 0.1%
2355 18
< 0.1%
2354 9
< 0.1%
2353 19
< 0.1%
2352 14
< 0.1%
2351 20
< 0.1%

DEP_DELAY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct588
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.138262
Minimum-81
Maximum1512
Zeros4850
Zeros (%)5.0%
Negative59436
Negative (%)61.0%
Memory size1.5 MiB
2025-07-08T21:00:12.715254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-81
5-th percentile-10
Q1-6
median-2
Q36
95-th percentile71
Maximum1512
Range1593
Interquartile range (IQR)12

Descriptive statistics

Standard deviation49.298681
Coefficient of variation (CV)4.8626363
Kurtosis190.44197
Mean10.138262
Median Absolute Deviation (MAD)4
Skewness10.815252
Sum987193
Variance2430.36
MonotonicityNot monotonic
2025-07-08T21:00:12.788254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 8089
 
8.3%
-4 7515
 
7.7%
-3 7076
 
7.3%
-6 6339
 
6.5%
-2 6270
 
6.4%
-1 5498
 
5.6%
-7 5044
 
5.2%
0 4850
 
5.0%
-8 3978
 
4.1%
-9 2883
 
3.0%
Other values (578) 39831
40.9%
ValueCountFrequency (%)
-81 1
 
< 0.1%
-42 2
 
< 0.1%
-38 1
 
< 0.1%
-36 1
 
< 0.1%
-35 2
 
< 0.1%
-34 2
 
< 0.1%
-32 1
 
< 0.1%
-29 5
< 0.1%
-28 1
 
< 0.1%
-27 8
< 0.1%
ValueCountFrequency (%)
1512 1
< 0.1%
1469 1
< 0.1%
1441 1
< 0.1%
1376 1
< 0.1%
1286 1
< 0.1%
1256 1
< 0.1%
1255 1
< 0.1%
1214 1
< 0.1%
1211 1
< 0.1%
1205 1
< 0.1%

TAXI_OUT
Real number (ℝ)

Distinct135
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.680045
Minimum1
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:12.861262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q111
median14
Q319
95-th percentile33
Maximum163
Range162
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.1710014
Coefficient of variation (CV)0.54981875
Kurtosis23.059615
Mean16.680045
Median Absolute Deviation (MAD)4
Skewness3.4143561
Sum1624186
Variance84.107267
MonotonicityNot monotonic
2025-07-08T21:00:12.937255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 8093
 
8.3%
13 7825
 
8.0%
11 7818
 
8.0%
14 7156
 
7.3%
10 7067
 
7.3%
15 6460
 
6.6%
16 5450
 
5.6%
9 5378
 
5.5%
17 4749
 
4.9%
18 4013
 
4.1%
Other values (125) 33364
34.3%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 3
 
< 0.1%
3 20
 
< 0.1%
4 83
 
0.1%
5 238
 
0.2%
6 780
 
0.8%
7 1803
 
1.9%
8 3500
3.6%
9 5378
5.5%
10 7067
7.3%
ValueCountFrequency (%)
163 1
< 0.1%
162 1
< 0.1%
158 1
< 0.1%
157 2
< 0.1%
151 1
< 0.1%
150 1
< 0.1%
147 1
< 0.1%
143 2
< 0.1%
142 2
< 0.1%
139 1
< 0.1%

WHEELS_OFF
Real number (ℝ)

HIGH CORRELATION 

Distinct1338
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1353.6206
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:13.223599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile618
Q1932
median1338
Q31752
95-th percentile2146
Maximum2400
Range2399
Interquartile range (IQR)820

Descriptive statistics

Standard deviation500.4479
Coefficient of variation (CV)0.36971061
Kurtosis-0.89992525
Mean1353.6206
Median Absolute Deviation (MAD)410
Skewness0.0066597414
Sum1.318061 × 108
Variance250448.1
MonotonicityNot monotonic
2025-07-08T21:00:13.295600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611 189
 
0.2%
610 164
 
0.2%
608 163
 
0.2%
615 160
 
0.2%
712 149
 
0.2%
715 148
 
0.2%
711 148
 
0.2%
619 147
 
0.2%
607 146
 
0.1%
613 146
 
0.1%
Other values (1328) 95813
98.4%
ValueCountFrequency (%)
1 22
< 0.1%
2 19
< 0.1%
3 14
< 0.1%
4 12
< 0.1%
5 20
< 0.1%
6 11
< 0.1%
7 14
< 0.1%
8 18
< 0.1%
9 14
< 0.1%
10 23
< 0.1%
ValueCountFrequency (%)
2400 9
 
< 0.1%
2359 23
< 0.1%
2358 17
< 0.1%
2357 12
< 0.1%
2356 24
< 0.1%
2355 14
< 0.1%
2354 18
< 0.1%
2353 12
< 0.1%
2352 20
< 0.1%
2351 18
< 0.1%

WHEELS_ON
Real number (ℝ)

HIGH CORRELATION 

Distinct1421
Distinct (%)1.5%
Missing28
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1463.905
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:13.366599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile654
Q11050
median1502
Q31909
95-th percentile2247
Maximum2400
Range2399
Interquartile range (IQR)859

Descriptive statistics

Standard deviation527.43063
Coefficient of variation (CV)0.36029019
Kurtosis-0.43288968
Mean1463.905
Median Absolute Deviation (MAD)416
Skewness-0.31773495
Sum1.4250384 × 108
Variance278183.07
MonotonicityNot monotonic
2025-07-08T21:00:13.438599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1820 136
 
0.1%
930 132
 
0.1%
1651 131
 
0.1%
1802 124
 
0.1%
1654 123
 
0.1%
1836 123
 
0.1%
1641 121
 
0.1%
1715 121
 
0.1%
2113 120
 
0.1%
1627 120
 
0.1%
Other values (1411) 96094
98.7%
ValueCountFrequency (%)
1 48
< 0.1%
2 49
0.1%
3 44
< 0.1%
4 47
< 0.1%
5 49
0.1%
6 36
< 0.1%
7 45
< 0.1%
8 36
< 0.1%
9 31
< 0.1%
10 40
< 0.1%
ValueCountFrequency (%)
2400 45
< 0.1%
2359 45
< 0.1%
2358 55
0.1%
2357 62
0.1%
2356 48
< 0.1%
2355 52
0.1%
2354 60
0.1%
2353 63
0.1%
2352 42
< 0.1%
2351 39
< 0.1%

TAXI_IN
Real number (ℝ)

Distinct120
Distinct (%)0.1%
Missing28
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.6952899
Minimum1
Maximum261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:13.508506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q39
95-th percentile18
Maximum261
Range260
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.4810638
Coefficient of variation (CV)0.84221178
Kurtosis92.748798
Mean7.6952899
Median Absolute Deviation (MAD)2
Skewness6.088001
Sum749098
Variance42.004188
MonotonicityNot monotonic
2025-07-08T21:00:13.581506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 15144
15.6%
5 14174
14.6%
6 11318
11.6%
3 10444
10.7%
7 9078
9.3%
8 6843
7.0%
9 5283
 
5.4%
10 4148
 
4.3%
11 3304
 
3.4%
2 3062
 
3.1%
Other values (110) 14547
14.9%
ValueCountFrequency (%)
1 183
 
0.2%
2 3062
 
3.1%
3 10444
10.7%
4 15144
15.6%
5 14174
14.6%
6 11318
11.6%
7 9078
9.3%
8 6843
7.0%
9 5283
 
5.4%
10 4148
 
4.3%
ValueCountFrequency (%)
261 1
< 0.1%
187 1
< 0.1%
184 1
< 0.1%
177 1
< 0.1%
173 1
< 0.1%
160 1
< 0.1%
155 1
< 0.1%
153 1
< 0.1%
151 1
< 0.1%
145 1
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct1327
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1490.4567
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:13.651506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile725
Q11108
median1517
Q31917
95-th percentile2254
Maximum2359
Range2358
Interquartile range (IQR)809

Descriptive statistics

Standard deviation512.11088
Coefficient of variation (CV)0.34359325
Kurtosis-0.4525262
Mean1490.4567
Median Absolute Deviation (MAD)405
Skewness-0.28411138
Sum1.4513024 × 108
Variance262257.55
MonotonicityNot monotonic
2025-07-08T21:00:13.721506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 317
 
0.3%
950 288
 
0.3%
1900 279
 
0.3%
1000 270
 
0.3%
2000 258
 
0.3%
1835 258
 
0.3%
1400 257
 
0.3%
1555 256
 
0.3%
2100 255
 
0.3%
1800 251
 
0.3%
Other values (1317) 94684
97.2%
ValueCountFrequency (%)
1 23
 
< 0.1%
2 27
 
< 0.1%
3 30
 
< 0.1%
4 20
 
< 0.1%
5 80
0.1%
6 27
 
< 0.1%
7 16
 
< 0.1%
8 23
 
< 0.1%
9 19
 
< 0.1%
10 84
0.1%
ValueCountFrequency (%)
2359 317
0.3%
2358 101
 
0.1%
2357 94
 
0.1%
2356 72
 
0.1%
2355 163
0.2%
2354 67
 
0.1%
2353 65
 
0.1%
2352 69
 
0.1%
2351 50
 
0.1%
2350 139
0.1%

ARR_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct1410
Distinct (%)1.4%
Missing28
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1467.1255
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:13.790506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile652
Q11053
median1506
Q31913
95-th percentile2249
Maximum2400
Range2399
Interquartile range (IQR)860

Descriptive statistics

Standard deviation532.51335
Coefficient of variation (CV)0.36296373
Kurtosis-0.35065461
Mean1467.1255
Median Absolute Deviation (MAD)413
Skewness-0.36097268
Sum1.4281733 × 108
Variance283570.47
MonotonicityNot monotonic
2025-07-08T21:00:13.862506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1115 130
 
0.1%
1343 129
 
0.1%
1655 123
 
0.1%
1020 123
 
0.1%
1052 122
 
0.1%
1840 122
 
0.1%
1230 122
 
0.1%
1547 121
 
0.1%
1550 121
 
0.1%
944 121
 
0.1%
Other values (1400) 96111
98.7%
ValueCountFrequency (%)
1 67
0.1%
2 50
0.1%
3 61
0.1%
4 42
< 0.1%
5 62
0.1%
6 40
< 0.1%
7 54
0.1%
8 39
< 0.1%
9 48
< 0.1%
10 60
0.1%
ValueCountFrequency (%)
2400 48
< 0.1%
2359 43
< 0.1%
2358 71
0.1%
2357 59
0.1%
2356 56
0.1%
2355 53
0.1%
2354 59
0.1%
2353 62
0.1%
2352 63
0.1%
2351 68
0.1%

ARR_DELAY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct613
Distinct (%)0.6%
Missing225
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.3537592
Minimum-88
Maximum1520
Zeros1853
Zeros (%)1.9%
Negative62508
Negative (%)64.2%
Memory size1.5 MiB
2025-07-08T21:00:13.935332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-88
5-th percentile-27
Q1-15
median-7
Q37
95-th percentile70
Maximum1520
Range1608
Interquartile range (IQR)22

Descriptive statistics

Standard deviation51.360244
Coefficient of variation (CV)11.796758
Kurtosis163.6387
Mean4.3537592
Median Absolute Deviation (MAD)10
Skewness9.7045591
Sum422959
Variance2637.8747
MonotonicityNot monotonic
2025-07-08T21:00:14.012614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11 2861
 
2.9%
-12 2845
 
2.9%
-13 2834
 
2.9%
-9 2825
 
2.9%
-10 2787
 
2.9%
-7 2753
 
2.8%
-14 2747
 
2.8%
-8 2690
 
2.8%
-15 2631
 
2.7%
-16 2534
 
2.6%
Other values (603) 69641
71.5%
ValueCountFrequency (%)
-88 1
 
< 0.1%
-68 1
 
< 0.1%
-67 1
 
< 0.1%
-66 2
 
< 0.1%
-63 1
 
< 0.1%
-62 2
 
< 0.1%
-61 4
< 0.1%
-60 3
 
< 0.1%
-59 9
< 0.1%
-58 1
 
< 0.1%
ValueCountFrequency (%)
1520 1
< 0.1%
1473 1
< 0.1%
1458 1
< 0.1%
1371 1
< 0.1%
1285 1
< 0.1%
1270 1
< 0.1%
1247 1
< 0.1%
1207 1
< 0.1%
1200 1
< 0.1%
1199 1
< 0.1%

CANCELLED
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
0.0
97373 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters292119
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 97373
100.0%

Length

2025-07-08T21:00:14.083687image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T21:00:14.137131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 97373
100.0%

Most occurring characters

ValueCountFrequency (%)
0 194746
66.7%
. 97373
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 194746
66.7%
. 97373
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 194746
66.7%
. 97373
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 194746
66.7%
. 97373
33.3%

CANCELLATION_CODE
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing97373
Missing (%)100.0%
Memory size1.5 MiB

DIVERTED
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.6 MiB
0.0
97148 
1.0
 
225

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters292119
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 97148
99.8%
1.0 225
 
0.2%

Length

2025-07-08T21:00:14.186131image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T21:00:14.235132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 97148
99.8%
1.0 225
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 194521
66.6%
. 97373
33.3%
1 225
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 194521
66.6%
. 97373
33.3%
1 225
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 194521
66.6%
. 97373
33.3%
1 225
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 292119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 194521
66.6%
. 97373
33.3%
1 225
 
0.1%

CRS_ELAPSED_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct501
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.8576
Minimum20
Maximum695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:14.290130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile64
Q190
median125
Q3173
95-th percentile301
Maximum695
Range675
Interquartile range (IQR)83

Descriptive statistics

Standard deviation71.980176
Coefficient of variation (CV)0.50385962
Kurtosis2.5880795
Mean142.8576
Median Absolute Deviation (MAD)40
Skewness1.444716
Sum13910473
Variance5181.1458
MonotonicityNot monotonic
2025-07-08T21:00:14.365130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 1864
 
1.9%
85 1766
 
1.8%
80 1679
 
1.7%
70 1516
 
1.6%
75 1453
 
1.5%
95 1420
 
1.5%
115 1235
 
1.3%
105 1230
 
1.3%
120 1202
 
1.2%
110 1195
 
1.2%
Other values (491) 82813
85.0%
ValueCountFrequency (%)
20 3
< 0.1%
21 2
< 0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
25 3
< 0.1%
26 3
< 0.1%
29 1
 
< 0.1%
30 2
< 0.1%
32 4
< 0.1%
33 3
< 0.1%
ValueCountFrequency (%)
695 1
< 0.1%
684 1
< 0.1%
680 1
< 0.1%
675 2
< 0.1%
670 2
< 0.1%
665 1
< 0.1%
660 2
< 0.1%
652 1
< 0.1%
650 2
< 0.1%
646 1
< 0.1%

ELAPSED_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct514
Distinct (%)0.5%
Missing225
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean137.07864
Minimum17
Maximum706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:14.438130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile56
Q185
median120
Q3168
95-th percentile293
Maximum706
Range689
Interquartile range (IQR)83

Descriptive statistics

Standard deviation72.041746
Coefficient of variation (CV)0.52555048
Kurtosis2.5343943
Mean137.07864
Median Absolute Deviation (MAD)40
Skewness1.4217463
Sum13316916
Variance5190.0132
MonotonicityNot monotonic
2025-07-08T21:00:14.511400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87 798
 
0.8%
79 780
 
0.8%
85 778
 
0.8%
83 773
 
0.8%
76 764
 
0.8%
82 760
 
0.8%
81 757
 
0.8%
75 755
 
0.8%
72 750
 
0.8%
77 747
 
0.8%
Other values (504) 89486
91.9%
ValueCountFrequency (%)
17 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
22 2
 
< 0.1%
23 1
 
< 0.1%
24 2
 
< 0.1%
25 5
< 0.1%
26 3
 
< 0.1%
28 9
< 0.1%
ValueCountFrequency (%)
706 1
< 0.1%
696 1
< 0.1%
687 1
< 0.1%
682 1
< 0.1%
676 1
< 0.1%
668 1
< 0.1%
657 1
< 0.1%
648 1
< 0.1%
644 1
< 0.1%
641 1
< 0.1%

AIR_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct492
Distinct (%)0.5%
Missing225
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean112.71605
Minimum9
Maximum669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:14.585399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile35
Q162
median95
Q3142
95-th percentile267
Maximum669
Range660
Interquartile range (IQR)80

Descriptive statistics

Standard deviation70.10449
Coefficient of variation (CV)0.62195658
Kurtosis2.6192588
Mean112.71605
Median Absolute Deviation (MAD)38
Skewness1.4556215
Sum10950139
Variance4914.6396
MonotonicityNot monotonic
2025-07-08T21:00:14.658399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 860
 
0.9%
43 858
 
0.9%
63 851
 
0.9%
65 835
 
0.9%
60 832
 
0.9%
58 828
 
0.9%
55 827
 
0.8%
59 824
 
0.8%
53 818
 
0.8%
66 811
 
0.8%
Other values (482) 88804
91.2%
ValueCountFrequency (%)
9 4
 
< 0.1%
10 4
 
< 0.1%
11 2
 
< 0.1%
12 2
 
< 0.1%
13 4
 
< 0.1%
14 3
 
< 0.1%
15 6
 
< 0.1%
16 15
 
< 0.1%
17 29
< 0.1%
18 49
0.1%
ValueCountFrequency (%)
669 1
< 0.1%
659 1
< 0.1%
657 1
< 0.1%
654 2
< 0.1%
634 1
< 0.1%
623 1
< 0.1%
621 1
< 0.1%
614 1
< 0.1%
611 1
< 0.1%
606 1
< 0.1%

DISTANCE
Real number (ℝ)

HIGH CORRELATION 

Distinct1607
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean814.12611
Minimum29
Maximum5812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:14.731400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile175
Q1387
median657
Q31050
95-th percentile2158
Maximum5812
Range5783
Interquartile range (IQR)663

Descriptive statistics

Standard deviation591.63469
Coefficient of variation (CV)0.72671136
Kurtosis2.8922065
Mean814.12611
Median Absolute Deviation (MAD)320
Skewness1.5081882
Sum79273902
Variance350031.61
MonotonicityNot monotonic
2025-07-08T21:00:14.803399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 618
 
0.6%
399 485
 
0.5%
296 481
 
0.5%
594 409
 
0.4%
224 398
 
0.4%
404 391
 
0.4%
214 384
 
0.4%
862 353
 
0.4%
733 352
 
0.4%
588 347
 
0.4%
Other values (1597) 93155
95.7%
ValueCountFrequency (%)
29 1
 
< 0.1%
30 1
 
< 0.1%
31 12
 
< 0.1%
41 2
 
< 0.1%
45 10
 
< 0.1%
50 3
 
< 0.1%
54 2
 
< 0.1%
61 4
 
< 0.1%
66 6
 
< 0.1%
67 56
0.1%
ValueCountFrequency (%)
5812 1
 
< 0.1%
5095 5
 
< 0.1%
4983 13
< 0.1%
4962 3
 
< 0.1%
4904 2
 
< 0.1%
4817 4
 
< 0.1%
4757 1
 
< 0.1%
4678 2
 
< 0.1%
4502 12
< 0.1%
4475 1
 
< 0.1%

DELAY_DUE_CARRIER
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct422
Distinct (%)2.3%
Missing79381
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean24.91285
Minimum0
Maximum1473
Zeros8088
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:14.873399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q323
95-th percentile105
Maximum1473
Range1473
Interquartile range (IQR)23

Descriptive statistics

Standard deviation71.923245
Coefficient of variation (CV)2.8869938
Kurtosis104.50496
Mean24.91285
Median Absolute Deviation (MAD)4
Skewness8.6833266
Sum448232
Variance5172.9531
MonotonicityNot monotonic
2025-07-08T21:00:14.948399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8088
 
8.3%
2 312
 
0.3%
6 300
 
0.3%
3 297
 
0.3%
15 293
 
0.3%
5 285
 
0.3%
1 284
 
0.3%
16 276
 
0.3%
4 257
 
0.3%
9 256
 
0.3%
Other values (412) 7344
 
7.5%
(Missing) 79381
81.5%
ValueCountFrequency (%)
0 8088
8.3%
1 284
 
0.3%
2 312
 
0.3%
3 297
 
0.3%
4 257
 
0.3%
5 285
 
0.3%
6 300
 
0.3%
7 249
 
0.3%
8 240
 
0.2%
9 256
 
0.3%
ValueCountFrequency (%)
1473 1
< 0.1%
1458 1
< 0.1%
1376 1
< 0.1%
1256 1
< 0.1%
1155 1
< 0.1%
1147 1
< 0.1%
1121 1
< 0.1%
1115 1
< 0.1%
1088 1
< 0.1%
1066 1
< 0.1%

DELAY_DUE_WEATHER
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct224
Distinct (%)1.2%
Missing79381
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean3.8727768
Minimum0
Maximum1285
Zeros16957
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:15.024403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum1285
Range1285
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.007417
Coefficient of variation (CV)7.7482951
Kurtosis538.35721
Mean3.8727768
Median Absolute Deviation (MAD)0
Skewness18.894339
Sum69679
Variance900.4451
MonotonicityNot monotonic
2025-07-08T21:00:15.100403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16957
 
17.4%
2 24
 
< 0.1%
7 22
 
< 0.1%
16 22
 
< 0.1%
6 21
 
< 0.1%
8 20
 
< 0.1%
9 19
 
< 0.1%
3 19
 
< 0.1%
32 19
 
< 0.1%
18 18
 
< 0.1%
Other values (214) 851
 
0.9%
(Missing) 79381
81.5%
ValueCountFrequency (%)
0 16957
17.4%
1 17
 
< 0.1%
2 24
 
< 0.1%
3 19
 
< 0.1%
4 16
 
< 0.1%
5 17
 
< 0.1%
6 21
 
< 0.1%
7 22
 
< 0.1%
8 20
 
< 0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
1285 1
< 0.1%
1059 1
< 0.1%
1000 1
< 0.1%
989 1
< 0.1%
825 1
< 0.1%
780 1
< 0.1%
733 1
< 0.1%
586 1
< 0.1%
565 1
< 0.1%
554 1
< 0.1%

DELAY_DUE_NAS
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct252
Distinct (%)1.4%
Missing79381
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean13.325311
Minimum0
Maximum1207
Zeros9248
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:15.174403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317
95-th percentile56
Maximum1207
Range1207
Interquartile range (IQR)17

Descriptive statistics

Standard deviation33.717869
Coefficient of variation (CV)2.5303626
Kurtosis260.9311
Mean13.325311
Median Absolute Deviation (MAD)0
Skewness11.443701
Sum239749
Variance1136.8947
MonotonicityNot monotonic
2025-07-08T21:00:15.251179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9248
 
9.5%
1 449
 
0.5%
15 345
 
0.4%
3 319
 
0.3%
17 311
 
0.3%
16 305
 
0.3%
2 303
 
0.3%
5 287
 
0.3%
6 287
 
0.3%
18 279
 
0.3%
Other values (242) 5859
 
6.0%
(Missing) 79381
81.5%
ValueCountFrequency (%)
0 9248
9.5%
1 449
 
0.5%
2 303
 
0.3%
3 319
 
0.3%
4 278
 
0.3%
5 287
 
0.3%
6 287
 
0.3%
7 260
 
0.3%
8 236
 
0.2%
9 203
 
0.2%
ValueCountFrequency (%)
1207 1
< 0.1%
998 1
< 0.1%
978 1
< 0.1%
880 1
< 0.1%
865 1
< 0.1%
818 1
< 0.1%
696 1
< 0.1%
539 1
< 0.1%
442 1
< 0.1%
439 1
< 0.1%

DELAY_DUE_SECURITY
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct45
Distinct (%)0.3%
Missing79381
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean0.13939529
Minimum0
Maximum249
Zeros17890
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:15.322179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum249
Range249
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.3412146
Coefficient of variation (CV)23.969351
Kurtosis3260.5075
Mean0.13939529
Median Absolute Deviation (MAD)0
Skewness50.587577
Sum2508
Variance11.163715
MonotonicityNot monotonic
2025-07-08T21:00:15.395179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 17890
 
18.4%
10 7
 
< 0.1%
1 7
 
< 0.1%
17 6
 
< 0.1%
14 5
 
< 0.1%
11 5
 
< 0.1%
15 5
 
< 0.1%
7 4
 
< 0.1%
5 4
 
< 0.1%
20 4
 
< 0.1%
Other values (35) 55
 
0.1%
(Missing) 79381
81.5%
ValueCountFrequency (%)
0 17890
18.4%
1 7
 
< 0.1%
2 2
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
5 4
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
249 1
< 0.1%
233 1
< 0.1%
117 1
< 0.1%
116 1
< 0.1%
91 1
< 0.1%
81 1
< 0.1%
59 1
< 0.1%
58 1
< 0.1%
54 1
< 0.1%
50 1
< 0.1%

DELAY_DUE_LATE_AIRCRAFT
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct352
Distinct (%)2.0%
Missing79381
Missing (%)81.5%
Infinite0
Infinite (%)0.0%
Mean24.84143
Minimum0
Maximum1018
Zeros9369
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-07-08T21:00:15.468179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q329
95-th percentile115
Maximum1018
Range1018
Interquartile range (IQR)29

Descriptive statistics

Standard deviation53.336648
Coefficient of variation (CV)2.1470845
Kurtosis70.032194
Mean24.84143
Median Absolute Deviation (MAD)0
Skewness6.1484477
Sum446947
Variance2844.798
MonotonicityNot monotonic
2025-07-08T21:00:15.541507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9369
 
9.6%
15 230
 
0.2%
16 200
 
0.2%
17 185
 
0.2%
18 184
 
0.2%
19 170
 
0.2%
12 166
 
0.2%
24 161
 
0.2%
20 156
 
0.2%
13 154
 
0.2%
Other values (342) 7017
 
7.2%
(Missing) 79381
81.5%
ValueCountFrequency (%)
0 9369
9.6%
1 129
 
0.1%
2 121
 
0.1%
3 126
 
0.1%
4 117
 
0.1%
5 132
 
0.1%
6 109
 
0.1%
7 134
 
0.1%
8 116
 
0.1%
9 127
 
0.1%
ValueCountFrequency (%)
1018 1
< 0.1%
1013 1
< 0.1%
1012 1
< 0.1%
960 1
< 0.1%
942 1
< 0.1%
889 1
< 0.1%
888 1
< 0.1%
840 1
< 0.1%
820 1
< 0.1%
802 1
< 0.1%

Interactions

2025-07-08T21:00:07.952137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.397940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.801911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.995275image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.193333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.423958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.927549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.193962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.371656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.589825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.081435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.276080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.502268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.757402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.265192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.518045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.804655image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.038742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.439934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.620947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.786010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.007895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.463549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.859922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.051700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.252560image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.486639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.986842image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.251964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.431656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.650829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.143176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.333140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.564950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.818944image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.326753image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.581147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.862008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.096742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.497344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.676473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.841698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.060431image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.518701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.913477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.105524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.308068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.548370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.045841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.306701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.488377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.709240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.202004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.388626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.622286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.878644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.383148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.637239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.917992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.149747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.548469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.731533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.891371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.112894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.576068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.967006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.158830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.363167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.606370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.103081image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.361701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.543673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.768413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.254015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.444649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.678664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.938308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.441153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.694624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.972682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.206271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.602915image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.783964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.942626image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.380643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.633335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.021459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.212830image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.421698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.665370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.165351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.416508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.600673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.825418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.307014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.498633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.739139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.002817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.498146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.752624image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.029474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.263949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.657451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.836831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.997053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.434973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.693698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.081761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.271243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.482698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.729370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.229391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.474736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.658359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.886420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.368262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.558896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.800486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.067817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.561175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.818870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.090760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.322767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.716756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.893468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.058614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.493307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.756417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.143760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.330584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.547698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.794370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.293378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.535742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.723883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.954161image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.430539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.620850image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.868948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.134004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.624017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.883952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.154764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.592365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.776522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.952636image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.120290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.545459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.818527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.201137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.385956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.607698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.855370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.355619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.589874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.779883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.017256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.488657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.678384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.928800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.196004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.684163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.942561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.214818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.647817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.834080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.004855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.179242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.599460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.877208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.258138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.440572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.665698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.917336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.417137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.646480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.836058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.076969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.543864image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.733383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.986857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.257004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.742162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.000732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.272645image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.702816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.889496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.057852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.233762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.650569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.937308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.315686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.497404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.724698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.978345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.475142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.700930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.891091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.131046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.600630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.789566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.046968image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.317004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.801389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.064032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.330925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.756814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.942902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.110853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.286762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.702567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:42.996104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.373985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.551538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.778698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.038337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.531775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.755499image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.948230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.190370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.652631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.846525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.103716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.374004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.859770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.125429image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.386692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.809528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.998108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.162862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.338185image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.754569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.057845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.432003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.606440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.839698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.098337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.593444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.813101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.005558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.245806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.708077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.907359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.164099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.435004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.919769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.186625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.450959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.864657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.052464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.220859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.391205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.811567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.121855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.494849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.675866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.903591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.165337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.657444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.872780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.067554image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.308005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.771668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.974632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.227099image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.722852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.983479image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.255194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.513892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.923654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.111778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.279860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.449204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.864931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.185191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.554896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.741203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.966217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.230337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.721186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.931574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.129869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.631683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.830668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.039568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.291310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.786620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.048846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.319596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.578289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:03.983672image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.168657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.338478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.505203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.921313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.247483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.617056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.805200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.029218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.297337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.784430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.990119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.188870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.691405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.889667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.103350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.354383image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.852336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.112232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.382551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.639374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.042385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.227943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.396502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.561330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:08.975069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.309098image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.674370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.866385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.093218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.357337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.844700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.048366image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.253870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.753261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.950797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.164909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.417564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.916344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.175809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.444834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.701128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.104316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.287680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.454851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.618680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:09.028075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.363577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.728602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.921385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.147217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.643855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.904651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.101371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.307870image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.809623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.005082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.223236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.475214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:58.974345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.232349image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.504454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.756188image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.159013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.342899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.510981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.670976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:09.085191image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.418577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.782877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:45.976386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.203217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.700363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:49.963322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.158218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.366441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.864905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.059011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.281979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.533541image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.034344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.293502image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.564809image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.814530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.216679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.400396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.567983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.728976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:09.139460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.472816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.835877image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.030385image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.259218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.758363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.020767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.211217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.420529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.918337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.111576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.337978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.589103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.092344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.348644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.626807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.868698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.274104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.456910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.623979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.785976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:09.196658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.526363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.888451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.083386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.314218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.813363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.077306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.266402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.478529image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:53.972777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.165584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.393203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.644262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.151797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.404819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.685337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.931630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.330163image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.512434image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.678419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.841976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:09.250648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:43.744363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:44.937451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:46.135091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:47.367217image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:48.868363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:50.132558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:51.316039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:52.531828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:54.024393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:55.217819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:56.445667image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:57.698179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T20:59:59.206797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:00.459412image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:01.745651image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:02.985639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:04.384814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:05.565546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:06.733008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-08T21:00:07.897132image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-08T21:00:15.810798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
AIRLINEAIRLINE_CODEAIRLINE_DOTAIR_TIMEARR_DELAYARR_TIMECRS_ARR_TIMECRS_DEP_TIMECRS_ELAPSED_TIMEDELAY_DUE_CARRIERDELAY_DUE_LATE_AIRCRAFTDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_WEATHERDEP_DELAYDEP_TIMEDISTANCEDIVERTEDDOT_CODEELAPSED_TIMEFL_NUMBERTAXI_INTAXI_OUTWHEELS_OFFWHEELS_ON
AIRLINE1.0001.0001.0000.1690.0260.0480.0500.0480.1740.0310.0270.0520.0050.0290.0260.0470.1770.0111.0000.1690.4150.0200.0910.0490.048
AIRLINE_CODE1.0001.0001.0000.1690.0260.0480.0500.0480.1740.0310.0270.0520.0050.0290.0260.0470.1770.0111.0000.1690.4150.0200.0910.0490.048
AIRLINE_DOT1.0001.0001.0000.1690.0260.0480.0500.0480.1740.0310.0270.0520.0050.0290.0260.0470.1770.0111.0000.1690.4150.0200.0910.0490.048
AIR_TIME0.1690.1690.1691.0000.0340.0440.052-0.0270.9840.013-0.0870.1760.016-0.0220.075-0.0280.9861.000-0.0640.979-0.3340.1250.071-0.0340.046
ARR_DELAY0.0260.0260.0260.0341.0000.1100.1120.130-0.0270.2100.3390.004-0.0110.1360.6550.1660.0041.000-0.0330.099-0.0390.1190.2690.1710.117
ARR_TIME0.0480.0480.0480.0440.1101.0000.8960.7300.039-0.0650.128-0.017-0.0150.0050.1320.7530.0490.036-0.0010.0440.001-0.0280.0280.7680.976
CRS_ARR_TIME0.0500.0500.0500.0520.1120.8961.0000.7940.049-0.0480.210-0.052-0.0130.0040.1440.7920.0600.0170.0010.049-0.007-0.0370.0250.8000.904
CRS_DEP_TIME0.0480.0480.048-0.0270.1300.7300.7941.000-0.033-0.0240.250-0.109-0.009-0.0010.1600.969-0.0220.0120.009-0.0310.000-0.0660.0040.9530.751
CRS_ELAPSED_TIME0.1740.1740.1740.984-0.0270.0390.049-0.0331.0000.030-0.0660.1200.015-0.0170.071-0.0340.9790.012-0.0230.974-0.3110.1630.112-0.0380.040
DELAY_DUE_CARRIER0.0310.0310.0310.0130.210-0.065-0.048-0.0240.0301.000-0.251-0.368-0.058-0.2260.303-0.0200.0391.000-0.062-0.042-0.024-0.122-0.139-0.026-0.066
DELAY_DUE_LATE_AIRCRAFT0.0270.0270.027-0.0870.3390.1280.2100.250-0.066-0.2511.000-0.297-0.013-0.0430.4520.278-0.0561.000-0.056-0.151-0.017-0.079-0.1980.2590.139
DELAY_DUE_NAS0.0520.0520.0520.1760.004-0.017-0.052-0.1090.120-0.368-0.2971.000-0.013-0.013-0.377-0.1250.1061.0000.1280.330-0.0560.2960.444-0.100-0.019
DELAY_DUE_SECURITY0.0050.0050.0050.016-0.011-0.015-0.013-0.0090.015-0.058-0.013-0.0131.000-0.0120.001-0.0100.0201.0000.0170.010-0.029-0.012-0.003-0.011-0.014
DELAY_DUE_WEATHER0.0290.0290.029-0.0220.1360.0050.004-0.001-0.017-0.226-0.043-0.013-0.0121.0000.1210.011-0.0231.0000.048-0.0060.040-0.0070.0630.0150.012
DEP_DELAY0.0260.0260.0260.0750.6550.1320.1440.1600.0710.3030.452-0.3770.0010.1211.0000.2030.0850.018-0.1740.074-0.084-0.0510.0200.1990.141
DEP_TIME0.0470.0470.047-0.0280.1660.7530.7920.969-0.034-0.0200.278-0.125-0.0100.0110.2031.000-0.0240.0130.003-0.0300.004-0.0640.0080.9840.774
DISTANCE0.1770.1770.1770.9860.0040.0490.060-0.0220.9790.039-0.0560.1060.020-0.0230.085-0.0241.0000.009-0.0870.961-0.3530.1100.055-0.0300.052
DIVERTED0.0110.0110.0111.0001.0000.0360.0170.0120.0121.0001.0001.0001.0001.0000.0180.0130.0091.0000.0131.0000.0080.0530.0140.0130.039
DOT_CODE1.0001.0001.000-0.064-0.033-0.0010.0010.009-0.023-0.062-0.0560.1280.0170.048-0.1740.003-0.0870.0131.000-0.0060.3340.2130.2790.008-0.003
ELAPSED_TIME0.1690.1690.1690.9790.0990.0440.049-0.0310.974-0.042-0.1510.3300.010-0.0060.074-0.0300.9611.000-0.0061.000-0.3050.2090.203-0.0320.046
FL_NUMBER0.4150.4150.415-0.334-0.0390.001-0.0070.000-0.311-0.024-0.017-0.056-0.0290.040-0.0840.004-0.3530.0080.334-0.3051.000-0.0280.0930.011-0.002
TAXI_IN0.0200.0200.0200.1250.119-0.028-0.037-0.0660.163-0.122-0.0790.296-0.012-0.007-0.051-0.0640.1100.0530.2130.209-0.0281.0000.067-0.063-0.036
TAXI_OUT0.0910.0910.0910.0710.2690.0280.0250.0040.112-0.139-0.1980.444-0.0030.0630.0200.0080.0550.0140.2790.2030.0930.0671.0000.0280.031
WHEELS_OFF0.0490.0490.049-0.0340.1710.7680.8000.953-0.038-0.0260.259-0.100-0.0110.0150.1990.984-0.0300.0130.008-0.0320.011-0.0630.0281.0000.789
WHEELS_ON0.0480.0480.0480.0460.1170.9760.9040.7510.040-0.0660.139-0.019-0.0140.0120.1410.7740.0520.039-0.0030.046-0.002-0.0360.0310.7891.000

Missing values

2025-07-08T21:00:09.364075image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-08T21:00:09.699445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-08T21:00:10.085669image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFT
02019-03-01Allegiant AirAllegiant Air: G4G4203681668PGDPunta Gorda, FLSPISpringfield, IL630620.0-10.09.0629.0731.07.0810738.0-32.00.0NaN0.0160.0138.0122.0994.0NaNNaNNaNNaNNaN
22022-04-12PSA Airlines Inc.PSA Airlines Inc.: OHOH203975560EWNNew Bern/Morehead/Beaufort, NCCLTCharlotte, NC625618.0-7.016.0634.0725.011.0744736.0-8.00.0NaN0.079.078.051.0221.0NaNNaNNaNNaNNaN
32021-10-13Southwest Airlines Co.Southwest Airlines Co.: WNWN193931944ABQAlbuquerque, NMDENDenver, CO17151740.025.015.01755.01844.07.018351851.016.00.0NaN0.080.071.049.0349.010.00.00.00.06.0
42022-06-05Southwest Airlines Co.Southwest Airlines Co.: WNWN193933081PITPittsburgh, PASTLSt. Louis, MO535535.00.012.0547.0609.06.0620615.0-5.00.0NaN0.0105.0100.082.0554.0NaNNaNNaNNaNNaN
52019-10-06Delta Air Lines Inc.Delta Air Lines Inc.: DLDL19790674LAXLos Angeles, CASEASeattle, WA11401138.0-2.014.01152.01405.011.014381416.0-22.00.0NaN0.0178.0158.0133.0954.0NaNNaNNaNNaNNaN
62020-03-17Southwest Airlines Co.Southwest Airlines Co.: WNWN19393443OAKOakland, CAHOUHouston, TX20002019.019.013.02032.0143.07.0135150.015.00.0NaN0.0215.0211.0191.01642.015.00.00.00.00.0
72020-02-06Southwest Airlines Co.Southwest Airlines Co.: WNWN193931779SFOSan Francisco, CASANSan Diego, CA13401338.0-2.013.01351.01456.03.015201459.0-21.00.0NaN0.0100.081.065.0447.0NaNNaNNaNNaNNaN
82019-03-11Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197902135DTWDetroit, MILASLas Vegas, NV714710.0-4.030.0740.0854.010.0847904.017.00.0NaN0.0273.0294.0254.01749.00.00.017.00.00.0
92022-07-24Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197901612DENDenver, COSLCSalt Lake City, UT20102038.028.016.02054.02159.05.021482204.016.00.0NaN0.098.086.065.0391.016.00.00.00.00.0
102021-07-10United Air Lines Inc.United Air Lines Inc.: UAUA199771185SLCSalt Lake City, UTIAHHouston, TX15021505.03.017.01522.01847.08.019001855.0-5.00.0NaN0.0178.0170.0145.01195.0NaNNaNNaNNaNNaN
FL_DATEAIRLINEAIRLINE_DOTAIRLINE_CODEDOT_CODEFL_NUMBERORIGINORIGIN_CITYDESTDEST_CITYCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTWHEELS_OFFWHEELS_ONTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDCRS_ELAPSED_TIMEELAPSED_TIMEAIR_TIMEDISTANCEDELAY_DUE_CARRIERDELAY_DUE_WEATHERDELAY_DUE_NASDELAY_DUE_SECURITYDELAY_DUE_LATE_AIRCRAFT
999902019-02-19Southwest Airlines Co.Southwest Airlines Co.: WNWN19393377DENDenver, COSEASeattle, WA855857.02.014.0911.01043.04.011001047.0-13.00.0NaN0.0185.0170.0152.01024.0NaNNaNNaNNaNNaN
999912019-02-21Southwest Airlines Co.Southwest Airlines Co.: WNWN193931752BWIBaltimore, MDTPATampa, FL825825.00.030.0855.01102.06.010501108.018.00.0NaN0.0145.0163.0127.0842.00.00.018.00.00.0
999922022-06-29Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197901643ATLAtlanta, GAMSNMadison, WI950946.0-4.09.0955.01036.08.010481044.0-4.00.0NaN0.0118.0118.0101.0707.0NaNNaNNaNNaNNaN
999932023-01-06Spirit Air LinesSpirit Air Lines: NKNK204161042SJUSan Juan, PRMCOOrlando, FL21302225.055.014.02239.017.039.0235056.066.00.0NaN0.0200.0211.0158.01189.00.00.011.00.055.0
999942023-08-05United Air Lines Inc.United Air Lines Inc.: UAUA199771478LASLas Vegas, NVIAHHouston, TX10231016.0-7.030.01046.01517.03.015331520.0-13.00.0NaN0.0190.0184.0151.01222.0NaNNaNNaNNaNNaN
999952019-02-26Delta Air Lines Inc.Delta Air Lines Inc.: DLDL197902229CHSCharleston, SCATLAtlanta, GA12441241.0-3.013.01254.01344.010.014081354.0-14.00.0NaN0.084.073.050.0259.0NaNNaNNaNNaNNaN
999962023-02-10Southwest Airlines Co.Southwest Airlines Co.: WNWN193932977MDWChicago, ILLAXLos Angeles, CA9251134.0129.019.01153.01322.011.012051333.088.00.0NaN0.0280.0239.0209.01750.088.00.00.00.00.0
999972019-06-26Endeavor Air Inc.Endeavor Air Inc.: 9E9E203635003ORFNorfolk, VAJFKNew York, NY17121715.03.025.01740.01831.06.018451837.0-8.00.0NaN0.093.082.051.0290.0NaNNaNNaNNaNNaN
999982023-06-22Endeavor Air Inc.Endeavor Air Inc.: 9E9E203635173CVGCincinnati, OHLGANew York, NY14331436.03.014.01450.01617.09.016401626.0-14.00.0NaN0.0127.0110.087.0585.0NaNNaNNaNNaNNaN
999992022-01-07Endeavor Air Inc.Endeavor Air Inc.: 9E9E203635227ATLAtlanta, GASHVShreveport, LA13451420.035.010.01430.01456.04.014381500.022.00.0NaN0.0113.0100.086.0551.00.00.00.00.022.0